Sequential Labelling with Active Learning to Extract Information about Disasters
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2017-08-29
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en
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Learning from past incidents has a great importance for disaster managers. Estimation of the outcomes beforehand can improve preparations for the next incidents. To make this a less labour-intensive task, we aim to automate extracting information from past events. We focus on extracting critical information about flooding events from newspaper articles as our use case. We treat this information extraction
task as a sequential labelling task and create an ensemble of two supervised machine
learning algorithms, namely Conditional Random Fields and Structured Support
Vector Machines, to achieve our goal. However, supervised learning requires
manually annotated training data, which is very expensive and time-consuming to
obtain. To reduce the need for manual annotation, Active Learning, a human-in-the-
loop method, is explored. We obtain improvement on f1-score up to 25% and observe that Active Learning drastically reduces the effort required by annotation.
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Faculteit der Sociale Wetenschappen